The wide adoption of smart phones and digital cameras has caused an explosion in the number of digital images. Digital images can be viewed, shared over the Internet, and posted on mobile applications and social networks using computer devices. Images can also be incorporated into photo products such as photographic prints, greeting cards, photo books, photo calendars, photo mug, photo T-shirt, and so on. In electronic forms or on physical products, images are often mixed with text and other design elements, and laid out in a particular fashion to tell a story.
Digital images often contain significant objects and people's faces. Creating photo blogs or high-quality image products, such as photobooks and greeting cards, naturally requires proper consideration of those objects and people's faces. For example, important people such as family members should have their faces prominently shown in image products while strangers' faces should be minimized. In another example, while pictures of different faces at a same scene can be included in an image-based product, the pictures of a same person at a same scene should normally be trimmed to allow only the best one(s) to be presented. Examples of significant objects include people's clothing, daily objects such as furniture, a birthday cake, and a soccer ball, and natural or man-made landmarks.
Faces need to be detected and group based on persons' identities before they can be properly selected and placed in image products. Most conventional face detection techniques concentrate on face recognition, assuming that a region of an image containing a single face has already been detected and extracted and will be provided as an input. Common face detection methods include knowledge-based methods; feature-invariant approaches, such as the identification of facial features, texture and skin color; template matching methods, both fixed and deformable; and appearance based methods. After faces are detected, face images of each individual can be categorized into a group regardless whether the identity of the individual is known or not. For example, if two individuals Person A and Person B are detected in ten images. Each of the images can be categorized or tagged one of the four types: A only; B only, A and B; or neither A nor B. Algorithmically, the tagging of face images require training based one face images of known persons (or face models), for example, the face images of family members or friends of a user who uploaded the images.
To save users' time, technologies have been developed to automate the creation of photo-product designs. These automatic methods are facing increased challenges as people take more digital photos. A single average vacation trip nowadays can easily produce thousands of photos. Automatic sorting, analyzing, grouping, and laying out such a great number of photos in the correct and meaningful manner are an immense task.
There is therefore a need for more accurately recognizing and grouping face images and other objects and incorporating them into photo-product designs and other imaging applications.